Real-time detection of potable-reclaimed water pipe cross-connection events by conventional water quality sensors using machine learning methods

2019 ◽  
Vol 238 ◽  
pp. 201-209 ◽  
Author(s):  
Xiyan Xu ◽  
Ying Liu ◽  
Shuming Liu ◽  
Junyu Li ◽  
Guancheng Guo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Real Time ◽  
Reclaimed Water ◽  
Water Pipe ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Real Time Detection ◽  
Water Quality Sensors ◽  
Cross Connection

Hybrid Non-Dominated Sorting Genetic Algorithm

2018 ◽  
pp. 264-286 ◽  
Author(s):  
Sankhadeep Chatterjee ◽  
Sarbartha Sarkar ◽  
Nilanjan Dey ◽  
Amira S. Ashour ◽  
Soumya Sen
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Genetic Algorithm ◽  
Water Quality ◽  
Quality Prediction ◽  
Learning Methods ◽  
Nsga Ii ◽  
Water Quality Prediction ◽  
Machine Learning Methods ◽  
Precautionary Measures

Water pollution due to industrial and domestic reasons is highly affecting the water quality. In undeveloped and developed countries, it has become a major reason behind a number of water borne diseases. Poor public health is putting an extra economic liability in order to deploy precautionary measures against these diseases. Recent research works have been directed toward more sustainable solutions to this problem. It has been revealed that good quality of water supply can not only improve the public health, it also accelerates economic growth of a geographical location as well. Water quality prediction using machine learning methods is still at its primitive stage. Besides, most of the studies did not follow any national or international standard for water quality prediction. In the current work, both the problems have been addressed. First, advanced machine learning methods, namely Artificial Neural Networks (ANNs) supported by a well-known multi-objective optimization algorithm called the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) has been used to classify the water samples into two different classes. Secondly, Indian national standard for water quality (IS 10500:2012) has been utilized for this classification task. The hybrid NN-NSGA-II model is compared with another two well-known meta-heuristic supported ANN classifiers, namely ANN trained by Genetic Algorithm (NN-GA) and by Particle Swarm Optimization (NN-PSO). Apart from that, the support vector machine (SVM) has also been included in the comparative study. Besides analysing the performance based on several performance measuring methods, the statistical significance of the results obtained by NN-NSGA-II has been judged by performing Wilcoxon rank sum test with 5% confidence level. Results have indicated the ingenuity of the proposed NN-NSGA-II model over the other classifiers under current study.

scholarly journals Real-time prediction of inpatient length of stay for discharge prioritization

2015 ◽  
Vol 23 (e1) ◽  
pp. e2-e10 ◽  
Author(s):  
Sean Barnes ◽  
Eric Hamrock ◽  
Matthew Toerper ◽  
Sauleh Siddiqui ◽  
Scott Levin
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Health Information ◽  
Patient Flow ◽  
Supervised Machine Learning ◽  
Youden Index ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
High Resource ◽  
Sensitivity Specificity

Abstract Objective Hospitals are challenged to provide timely patient care while maintaining high resource utilization. This has prompted hospital initiatives to increase patient flow and minimize nonvalue added care time. Real-time demand capacity management (RTDC) is one such initiative whereby clinicians convene each morning to predict patients able to leave the same day and prioritize their remaining tasks for early discharge. Our objective is to automate and improve these discharge predictions by applying supervised machine learning methods to readily available health information. Materials and Methods The authors use supervised machine learning methods to predict patients’ likelihood of discharge by 2 p.m. and by midnight each day for an inpatient medical unit. Using data collected over 8000 patient stays and 20 000 patient days, the predictive performance of the model is compared to clinicians using sensitivity, specificity, Youden’s Index (i.e., sensitivity + specificity – 1), and aggregate accuracy measures. Results The model compared to clinician predictions demonstrated significantly higher sensitivity ( P  < .01), lower specificity ( P  < .01), and a comparable Youden Index ( P  > .10). Early discharges were less predictable than midnight discharges. The model was more accurate than clinicians in predicting the total number of daily discharges and capable of ranking patients closest to future discharge. Conclusions There is potential to use readily available health information to predict daily patient discharges with accuracies comparable to clinician predictions. This approach may be used to automate and support daily RTDC predictions aimed at improving patient flow.

scholarly journals Multiclass ECG Signal Analysis Using Global Average-Based 2-D Convolutional Neural Network Modeling

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 170
Author(s):  
Muhammad Wasimuddin ◽  
Khaled Elleithy ◽  
Abdelshakour Abuzneid ◽  
Miad Faezipour ◽  
Omar Abuzaghleh
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Signal Analysis ◽  
Neural Network Modeling ◽  
Ecg Signal ◽  
Analysis Model ◽  
Learning Methods ◽  
Machine Learning Methods

Cardiovascular diseases have been reported to be the leading cause of mortality across the globe. Among such diseases, Myocardial Infarction (MI), also known as “heart attack”, is of main interest among researchers, as its early diagnosis can prevent life threatening cardiac conditions and potentially save human lives. Analyzing the Electrocardiogram (ECG) can provide valuable diagnostic information to detect different types of cardiac arrhythmia. Real-time ECG monitoring systems with advanced machine learning methods provide information about the health status in real-time and have improved user’s experience. However, advanced machine learning methods have put a burden on portable and wearable devices due to their high computing requirements. We present an improved, less complex Convolutional Neural Network (CNN)-based classifier model that identifies multiple arrhythmia types using the two-dimensional image of the ECG wave in real-time. The proposed model is presented as a three-layer ECG signal analysis model that can potentially be adopted in real-time portable and wearable monitoring devices. We have designed, implemented, and simulated the proposed CNN network using Matlab. We also present the hardware implementation of the proposed method to validate its adaptability in real-time wearable systems. The European ST-T database recorded with single lead L3 is used to validate the CNN classifier and achieved an accuracy of 99.23%, outperforming most existing solutions.

Application of machine learning methods to assess progressive cavity pumps (PCPs) performance in coal seam gas (CSG) wells

2020 ◽  
Vol 60 (1) ◽  
pp. 197
Author(s):  
Fahd Saghir ◽  
M. E. Gonzalez Perdomo ◽  
Peter Behrenbruch
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Real Time ◽  
Time Series Data ◽  
Series Data ◽  
Coal Seam Gas ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Artificial Lift ◽  
Image Conversion

In Queensland, progressive cavity pumps (PCPs) are the artificial lift method of choice in coal seam gas (CSG) wells, and this choice of artificial lift production stems from the ability of PCPs to better manage the production of liquids with suspended solids. As with any mechanical pumping system, PCPs are prone to natural wear and tear over their operational life, and with the production of coal fines and inter-burden, the run life of PCPs in CSG wells is significantly reduced. Another factor to consider with the use of PCPs is their reliability. As per the CSG production data available through the Queensland Government Data Portal, there are approximately 6400 wells operational in the state as of December 2018. This number is expected to grow significantly over the next decade to meet both international and domestic gas utilisation requirements. Operators supervising these wells rely on a reactive or exception-based approach to manage well performance. In order to efficiently operate thousands of PCP wells, it is pertinent that a benchmark methodology is devised to autonomously monitor PCP performance and allow operators to manage wells by exception. In this study, we will cover the application of machine learning methods to understand anomalous PCP behaviour and overall pump performance based on the analysis of multivariate time-series data. An innovative time-series data approximation and image conversion technique will be discussed in this paper, along with machine learning methods, which will focus on a scalable and autonomous approach to cluster PCP performance and detection of anomalous pump behaviour in near real-time. Results from this study show that clustering real-time data based on converted time-series images helps to pro-actively detect change in PCP performance. Discovery of anomalous multivariate events is also achieved through time-series image conversion. This study also demonstrates that clustering time-series data noticeably improves the real-time monitoring capabilities of PCP performance through improved visual analytics.

Machine learning methods for better water quality prediction

2019 ◽  
Vol 578 ◽  
pp. 124084 ◽  
Author(s):  
Ali Najah Ahmed ◽  
Faridah Binti Othman ◽  
Haitham Abdulmohsin Afan ◽  
Rusul Khaleel Ibrahim ◽  
Chow Ming Fai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Quality Prediction ◽  
Learning Methods ◽  
Water Quality Prediction ◽  
Machine Learning Methods
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